Emotion Detection Computer Vision Project
Emotion Detection Model for Facial Expressions
Project Description:
In this project, we developed an Emotion Detection Model using a curated dataset of 715 facial images, aiming to accurately recognize and categorize expressions into five distinct emotion classes. The emotion classes include Happy, Sad, Fearful, Angry, and Neutral.
Objectives:
- Train a robust machine learning model capable of accurately detecting and classifying facial expressions in real-time.
- Implement emotion detection to enhance user experience in applications such as human-computer interaction, virtual assistants, and emotion-aware systems.
Methodology:
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Data Collection and Preprocessing:
- Assembled a diverse dataset of 715 images featuring individuals expressing different emotions.
- Employed Roboflow for efficient data preprocessing, handling image augmentation and normalization.
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Model Architecture:
- Utilized a convolutional neural network (CNN) architecture to capture spatial hierarchies in facial features.
- Implemented a multi-class classification approach to categorize images into the predefined emotion classes.
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Training and Validation:
- Split the dataset into training and validation sets for model training and evaluation.
- Fine-tuned the model parameters to optimize accuracy and generalization.
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Model Evaluation:
- Evaluated the model's performance on an independent test set to assess its ability to generalize to unseen data.
- Analyzed confusion matrices and classification reports to understand the model's strengths and areas for improvement.
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Deployment and Integration:
- Deployed the trained emotion detection model for real-time inference.
- Integrated the model into applications, allowing users to interact with systems based on detected emotions.
Results: The developed Emotion Detection Model demonstrates high accuracy in recognizing and classifying facial expressions across the defined emotion classes. This project lays the foundation for integrating emotion-aware systems into various applications, fostering more intuitive and responsive interactions.
Trained Model API
This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.
YOLOv8
This project has a YOLOv8 model checkpoint available for inference with Roboflow Deploy. YOLOv8 is a new state-of-the-art real-time object detection model.
Cite This Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
emotion-detection-y0svj_dataset,
title = { Emotion Detection Dataset },
type = { Open Source Dataset },
author = { Computer Vision Projects },
howpublished = { \url{ https://universe.roboflow.com/computer-vision-projects-zhogq/emotion-detection-y0svj } },
url = { https://universe.roboflow.com/computer-vision-projects-zhogq/emotion-detection-y0svj },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2024 },
month = { jan },
note = { visited on 2024-05-15 },
}
Connect Your Model With Program Logic
Find utilities and guides to help you start using the Emotion Detection project in your project.